Data-driven soft sensor of downhole pressure for a gas-lift oil well

Downhole pressure is a key variable in the operation of gas-lift oil wells. However, maintaining and replacing downhole sensors is a challenging task. In this context, we design and implement a data-driven soft sensor to estimate online the downhole pressure based on other (seabed and platform) available measurements. Such application is based on a two-step procedure. In the first step, discrete-time black-box and gray-box NARX models are identified offline and independently using historical data. Both polynomial and neural models are obtained. In the second step, recursive predictions of these multiple models are combined with current measured data (of variables other than the downhole pressure) by means of an interacting bank of unscented Kalman filters. In doing so, a closed-loop model prediction is performed. Three issues are investigated in this paper concerning: (i) the usage of a filter bank rather than a single filter approach, (ii) the availability of seabed variables as inputs of the models compared to the case where only platform variables are available, and (iii) the employment of gray-box models in the filters. Experimental results along 7 months of tests indicate that such closed-loop scheme improves estimation accuracy and robustness compared to the free-run model prediction or to the use of a single unscented Kalman filter. The method employed in this paper can also be applied to other soft sensing applications in industry.